# Multi-organ metastatic breast cancer cell-based assay platform that models organotropic metastases using patient organoids in human tissue-derived ECMs to accelerate anti-metastatic drug development

> **NIH NIH R44** · XYLYX BIO, INC. · 2024 · $295,604

## Abstract

PROJECT ABSTRACT
Xylyx is developing a predictive multi-organ metastatic breast cancer cell-based assay platform to address the
lack of in-vitro models of organotropic metastatic breast cancer in the market and accelerate drug development.
Breast cancer is the most commonly diagnosed cancer in women, and most commonly metastasizes to bone,
liver, and lung. Metastasis causes ~90% of cancer deaths, and metastatic breast cancer remains the second
leading cause of death from cancer. Survival 5 years after diagnosis is 27%, and there is no cure. The
extracellular matrix (ECM) in bone, liver, and lung is known to play critical roles in metastatic invasion and
colonization. Animal models are poor predictors of metastasis in humans, and predictive in-vitro models of
metastatic breast cancer are not commercially available, leaving a significant unmet need and market gap/
opportunity for a physiologically-relevant in-vitro platform that enables high-fidelity cell-based phenotypic assays
in organotropic breast cancer metastases. This SBIR Fast Track will support development and validation studies
for commercialization of a multi-organ metastatic breast cancer cell-based assay platform containing engineered
organotropic metastases shown to be consistent with patient data. The technological innovation is the product’s
organotropic (bone, liver, lung) metastases stemming from proprietary methods for isolating and integrating
acellular human tissue ECMs with the tissue-specific properties of human tissues. Our approach integrates
breast cancer patient-derived organoids in standardized multi-organ tissue-specific primary human ECMs,
enabling predictive assays on organotropic metastases – a major competitive advantage over all existing assays,
which lack multi-organ human tissue-specificity. Our goal is to validate and commercialize a standard multi-organ
metastatic breast cancer cell-based assay platform for predictive in-vitro modeling of metastatic breast cancer
to reduce dependence on animal models and de-risk preclinical decision-making. Specific aims: (1) Perform
multi-omics and histomorphologic profiling of engineered human breast cancer bone/liver/lung organotropic
metastases; (2) Evaluate histologic, molecular, phenotypic effects of cancer-associated fibroblasts (CAFs) on
engineered organotropic metastases; (3) Evaluate quality and consistency of engineered human bone/liver/lung
organotropic metastases assay platform; (4) Test stage IV breast cancer standard-of-care drugs in combination
with therapies targeting matrix components. After successful completion of the Fast Track project, Xylyx will
commercialize the metastatic breast cancer assay platform for scientists in pharma companies in need of
predictive metastatic disease models for drug screening, thus reducing the massive costs associated with late-
stage attrition due to poor efficacy, and facilitating development of better treatment options for the 270,000+
patients diagnosed with metastatic brea...

## Key facts

- **NIH application ID:** 10920937
- **Project number:** 1R44CA287881-01A1
- **Recipient organization:** XYLYX BIO, INC.
- **Principal Investigator:** John David O'Neill
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $295,604
- **Award type:** 1
- **Project period:** 2024-06-06 → 2027-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10920937

## Citation

> US National Institutes of Health, RePORTER application 10920937, Multi-organ metastatic breast cancer cell-based assay platform that models organotropic metastases using patient organoids in human tissue-derived ECMs to accelerate anti-metastatic drug development (1R44CA287881-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10920937. Licensed CC0.

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